Text classification refers to the process of letting a computer learn to complete the classification of content by some classification algorithm under the classification system of text. Algorithms related to text classification have been applied to web classification, digital libraries, news recommendation, and other fields. Based on the characteristics of short text classification tasks, this study proposes a hybrid short text classical model based on multi-neural networks. By reconstructing the text features of the keywords extracted from the short text content, and using the vector fusion as the input of the multi-neural network model, the characteristics of the FastText model and the TextCNN model are taken into account. The experimental results show that compared with the current popular text classification algorithms, the multi-neural network hybrid short text classification model shows more superior algorithm performance on multiple indicators such as accuracy, recall, and F1 score.